Our group has approached the problem of prostate cancer classification and stratification using genome-wide expression analysis. Our recent data shows that expression patterns derived from a study of 12,000 human cDNA?s in 52 prostate tumors and 50 normal nontumor prostate samples can reliably distinguish tumor from normal, low from high Gleason Score tumors, and can accurately predict relapse following prostatectomy. We now wish to discover novel gene expression patterns that are linked to the underlying genetic abnormalities found in these tumors. It is our hypothesis that such gene expression patterns will allow one to discover those genes and gene products that are casually linked to tumor development and progression. To this end will use high-density Single Nucleotide Polymorphisms (SNP) arrays to analyze genetic loss in tumors for which expression data is already in-hand. In aims 1 and 2 we will use high-density SNP arrays to determine genomic-wide patterns of genetic loss in 52 prostate tumors for which gene expression data is already known. Using supervised methods of data analysis we will try to discern gene expression patterns that are correlated with regional loss-of-heterozygosity. This may allow the identification of the pathways casually linked to prostate cancer development, and ultimately to the identification of novel cancer therapeutic targets. We are able to use high-density array based methods of SNP analysis to discern the patterns of genetic loss in tumors for which only paraffin embedded tissue sections are available. This technology can thus be used to describe and categorize cancers wherein only fixed biopsy specimens exist. Therefore, in aim 3 we will use this technology to test the hypothesis that differences in the genetic composition of tumors in patients undergoing radiotherapy account for differences in patient outcome.